Automated sleep apnea detection from snoring and carotid pulse signals using an innovative neck wearable piezoelectric sensor

Yi Ping Chao, Hai Hua Chuang, Yu-Lun Lo, Shu Yi Huang, Wan Ting Zhan, Guo She Lee, Hsueh Yu Li, Liang Yu Shyu, Li Ang Lee*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

Abstract

This study introduces an innovative wearable neck piezoelectric sensor (NPS) that measures snoring vibrations and carotid pulsations, offering a significant advancement in sleep apnea syndrome (SAS) diagnosis. Utilizing advanced algorithms like discrete wavelet transform and dynamic thresholding, the NPS detects snoring events with 83% accuracy, comparable to polysomnography, and calculates key metrics such as the snoring index (SI) and normalized snoring vibration energy (SVE%). Unlike traditional methods, the SVE% from NPS directly correlates with subjective assessments of snoring severity. It also measures carotid pulsation metrics such as pulse rate and the standard deviation of normal-to-normal intervals, achieving 85% accuracy in sleep phase determination against polysomnography. Moreover, NPS surpasses traditional methods in SI and SVE% accuracy, closely aligning with clinical evaluations of SAS severity. This user-friendly technology automates the measurement of critical snoring metrics, transforming SAS diagnosis and treatment by enhancing accessibility and efficiency for healthcare providers and patients.

Original languageEnglish
Article number116102
JournalMeasurement: Journal of the International Measurement Confederation
Volume242
DOIs
StatePublished - 01 2025

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

  • Carotid artery pulsation
  • Multivariate categorical regression
  • Neck piezoelectric sensor
  • Sleep apnea syndrome
  • Snoring

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